def test_fred_parts(self):
import numpy as np
start = datetime(2010, 1, 1)
end = datetime(2013, 01, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
assert df.ix['2010-05-01'] == 217.23
t = np.array(df.CPIAUCSL.tolist())
assert np.issubdtype(t.dtype, np.floating)
assert t.shape == (37, )
# Test some older ones:
expected = [[576.7], [962.9], [684.7], [848.3], [933.3]]
result = web.get_data_fred("A09024USA144NNBR", start="1915").ix[:5]
assert (result.values == expected).all()
def test_fred_parts(self):
import numpy as np
start = datetime(2010, 1, 1)
end = datetime(2013, 01, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
assert df.ix["2010-05-01"] == 217.23
t = np.array(df.CPIAUCSL.tolist())
assert np.issubdtype(t.dtype, np.floating)
assert t.shape == (37,)
# Test some older ones:
expected = [[576.7], [962.9], [684.7], [848.3], [933.3]]
result = web.get_data_fred("A09024USA144NNBR", start="1915").ix[:5]
assert (result.values == expected).all()
def test_fred_part2(self):
expected = [[576.7],
[962.9],
[684.7],
[848.3],
[933.3]]
result = web.get_data_fred("A09024USA144NNBR", start="1915").ix[:5]
tm.assert_numpy_array_equal(result.values, np.array(expected))
def test_fred_part2(self):
expected = [[576.7],
[962.9],
[684.7],
[848.3],
[933.3]]
result = web.get_data_fred("A09024USA144NNBR", start="1915").ix[:5]
assert_array_equal(result.values, np.array(expected))
def test_fred_parts(self):
start = datetime(2010, 1, 1)
end = datetime(2013, 1, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
self.assertEqual(df.ix['2010-05-01'], 217.23)
t = df.CPIAUCSL.values
assert np.issubdtype(t.dtype, np.floating)
self.assertEqual(t.shape, (37,))
def test_fred_parts(self):
start = datetime(2010, 1, 1)
end = datetime(2013, 1, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
self.assertEqual(df.ix['2010-05-01'], 217.23)
t = df.CPIAUCSL.values
assert np.issubdtype(t.dtype, np.floating)
self.assertEqual(t.shape, (37, ))
def test_fred_parts(self):
raise nose.SkipTest('buggy as of 2/18/14; maybe a data revision?')
start = datetime(2010, 1, 1)
end = datetime(2013, 1, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
self.assertEqual(df.ix['2010-05-01'][0], 217.23)
t = df.CPIAUCSL.values
self.assertTrue(np.issubdtype(t.dtype, np.floating))
self.assertEqual(t.shape, (37,))
def test_fred_parts(self):
raise nose.SkipTest('buggy as of 2/18/14; maybe a data revision?')
start = datetime(2010, 1, 1)
end = datetime(2013, 1, 27)
df = web.get_data_fred("CPIAUCSL", start, end)
self.assertEqual(df.ix['2010-05-01'][0], 217.23)
t = df.CPIAUCSL.values
self.assertTrue(np.issubdtype(t.dtype, np.floating))
self.assertEqual(t.shape, (37, ))
bands to a Matplotlib Figure object.
"""
# load the NBER recession dates
NBER_Dates = pd.read_csv('NBER Dates.txt')
# for loop generates recession bands!
for i in range(NBER_Dates.shape[0]):
plt.axvspan(NBER_Dates['Peak'][i], NBER_Dates['Trough'][i],
facecolor='grey', alpha=0.5)
##### download data from FRED #####
# National Income: Compensation of Employees, Paid
COE = get_data_fred('COE', start='1947-01-01')
# Gross Domestic Product, 1 Decimal
GDP = get_data_fred('GDP', start='1947-01-01')
# combine into a DataFrame
data = pd.concat([COE, GDP], axis=1)
##### Construct measure of labor's share #####
# Divide COE by GDP
COE_share = data['COE'] / data['GDP']
##### plot the data with auto-ajusted ylim #####
# basic plot in one line of code!
# load the NBER recession dates
NBER_Dates = pd.read_csv('NBER Dates.txt')
# for loop generates recession bands!
for i in range(NBER_Dates.shape[0]):
plt.axvspan(NBER_Dates['Peak'][i],
NBER_Dates['Trough'][i],
facecolor='grey',
alpha=0.5)
##### download data from FRED #####
# Civilian unemployment rate (monthly, SA)
UNRATE_monthly = get_data_fred('UNRATE', start='1948-01-01')
# Convert to annual frequency by averaging across months
UNRATE_annual = UNRATE_monthly.resample('A', how='mean')
##### plot the historical unemployment rate #####
# basic plot in one line of code!
UNRATE_annual.plot()
# add labels, axes, title, etc
plt.xlabel("Year")
plt.ylabel("Percent")
plt.ylim(0, 10)
plt.title("Civilian unemployment rate (UNRATE), Seasonally adjusted\n" +\
"Source: U.S. Department of Labor, BLS (via FRED)",
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_fred
##### download data from FRED #####
# Real GDP per capita (2010 Dollars, annual, NSA)
USARGDPC = get_data_fred('USARGDPC', start='1960-01-01')
##### plot the data #####
# basic plot in one line of code!
ax = USARGDPC.plot(legend=False)
# add labels, axes, title, etc
ax.set_ylabel("2010 U.S. Dollars")
ax.set_yscale('log')
ax.set_ylim(8000, 64000)
ax.set_yticks([8000, 16000, 32000, 64000])
ax.set_yticklabels([8000, 16000, 32000, 64000])
ax.set_title("Real GDP per Person in the United States (USARGDPC)\nSource: U.S. Department of Labor, BLS (via FRED)", weight='bold')
ax.grid()
# load the NBER recession dates
NBER_Dates = pd.read_csv('NBER Dates.txt')
# for loop generates recession bands!
for i in range(NBER_Dates.shape[0]):
ax.axvspan(NBER_Dates['Peak'][i], NBER_Dates['Trough'][i], facecolor='grey', alpha=0.5)
# save the figure and display
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_fred
##### download data from FRED #####
# Consumer Price Index for All Urban Consumers: All Items (Monthly, SA)
CPIAUCSL = get_data_fred('CPIAUCSL', start='1947-01-01')
# Consumer Price Index for All Urban Consumers: All Items (Monthly, NSA)
CPIAUCNS = get_data_fred('CPIAUCNS', start='1913-01-01')
# Gross Domestic Product: Implicit Price Deflator (Quarterly, SA)
GDPDEF = get_data_fred('GDPDEF', start='1947-01-01')
##### Construct measures of inflation #####
# Inflation is measured as percentage change from one year ago
Inflation_CPIAUCSL = CPIAUCSL.pct_change(periods=12)
Inflation_CPIAUCNS = CPIAUCNS.pct_change(periods=12)
Inflation_GDPDEF = GDPDEF.pct_change(periods=4)
# Combine the three Series objects into a single DataFrame
Inflation_Measures = Inflation_CPIAUCNS
Inflation_Measures['CPIAUCSL'] = Inflation_CPIAUCSL
Inflation_Measures['GDPDEF'] = Inflation_GDPDEF
##### plot the data #####
# basic plot in one line of code!
Inflation_Measures.plot(markersize=3, style='o-', alpha=0.75)
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_yahoo, get_data_fred
##### download data #####
# Download the S&P 500
SP500 = get_data_yahoo('^GSPC', start='1950-01-03', end='2012-11-30')
# Download the CPI data
CPIAUCSL = get_data_fred('CPIAUCSL', start='1950-01-01')
##### resample S&P 500 data #####
# Need S&P 500 data to be monthly...note I am taking monthly averages
monthly_avg_SP500 = SP500.resample('MS', how='mean')
# Add the CPI data as a column to the monthly DataFrame
monthly_avg_SP500['CPIAUCSL'] = CPIAUCSL
##### Convert nominal values to real values #####
# express all prices in terms of the price level in Nov. 2012...
monthly_avg_SP500['Price Deflator'] = (monthly_avg_SP500['CPIAUCSL']['2012-11-01'] / monthly_avg_SP500['CPIAUCSL'])
monthly_avg_SP500['Close (Real)'] = monthly_avg_SP500['Close'] * monthly_avg_SP500['Price Deflator']
##### Nominal S&P 500 #####
# new figure
fig = plt.figure()
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_yahoo, get_data_fred
##### download data #####
# Download the S&P 500
SP500 = get_data_yahoo('^GSPC', start='1950-01-03', end='2012-11-30')
# Download the CPI data
CPIAUCSL = get_data_fred('CPIAUCSL', start='1950-01-01')
##### resample S&P 500 data #####
# Need S&P 500 data to be monthly...note I am taking monthly averages
monthly_avg_SP500 = SP500.resample('MS', how='mean')
# Add the CPI data as a column to the monthly DataFrame
monthly_avg_SP500['CPIAUCSL'] = CPIAUCSL
##### Convert nominal values to real values #####
# express all prices in terms of the price level in Nov. 2012...
monthly_avg_SP500['Price Deflator'] = (
monthly_avg_SP500['CPIAUCSL']['2012-11-01'] /
monthly_avg_SP500['CPIAUCSL'])
monthly_avg_SP500['Close (Real)'] = monthly_avg_SP500[
'Close'] * monthly_avg_SP500['Price Deflator']
##### Nominal S&P 500 #####
bands to a Matplotlib Figure object.
"""
# load the NBER recession dates
NBER_Dates = pd.read_csv('NBER Dates.txt')
# for loop generates recession bands!
for i in range(NBER_Dates.shape[0]):
plt.axvspan(NBER_Dates['Peak'][i], NBER_Dates['Trough'][i],
facecolor='grey', alpha=0.5)
##### download data from FRED #####
# Civilian unemployment rate (monthly, SA)
UNRATE_monthly = get_data_fred('UNRATE', start='1948-01-01')
# Convert to annual frequency by averaging across months
UNRATE_annual = UNRATE_monthly.resample('A', how='mean')
##### plot the historical unemployment rate #####
# basic plot in one line of code!
UNRATE_annual.plot()
# add labels, axes, title, etc
plt.xlabel("Year")
plt.ylabel("Percent")
plt.ylim(0, 10)
plt.title("Civilian unemployment rate (UNRATE), Seasonally adjusted\n" +\
"Source: U.S. Department of Labor, BLS (via FRED)",